import pymysql
import pandas as pd
import pymysql.cursors

class MysqlUtils(object):
    """数据库工具类
    """
    
    def __init__(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            user='root',
            passwd='root',
            db='lyy',
            port=3306,
            charset='utf8'
        )
        

class classIfication(object):
    
    def get_fina_indicator(self, conn):
        """获取财务表相关数据
        """
        cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)
        
        sql = """
        SELECT ts_code, ann_date, eps, total_revenue_ps, undist_profit_ps, gross_margin, fcff, fcfe, 
        tangible_asset, bps, grossprofit_margin, npta, roic FROM financial_data 
        WHERE ann_date >= '2023-01-01' and ann_date < '2024-01-01'
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        # print(ret)
        df = pd.DataFrame(ret)
        df1 = df.dropna(subset=['eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta', 'roic'])
        # 重建索引
        df1 = df1.reset_index(drop=False)
        # print(df1.head)
        return df1
    
    def get_daily(self, conn, df):
        cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)
        new_list = []
        
        for i in range(len(df['ts_code'])):
            ann_date_str = df['ann_date'][i].strftime('%Y%m%d')
            print(i)
            sql = 'select trade_date, closes from date_1 where ts_code = \'' + df['ts_code'][i] + '\' and trade_date > Date(\'' + ann_date_str + '\') order by trade_date limit 20' 
            # print(sql)
            cursor.execute(sql)
            ret = cursor.fetchall()
            # print(ret)
            df1 = pd.DataFrame(ret)
            # print(df1.head)
            try:
                if len(df1) > 0:
                    max_close = df1['closes'].max()
                    min_close = df1['closes'].min()
                    the_close = df1['closes'].iloc[1]
                    # 'eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta', 'roic
                    new_list.append({
                        'ts_code': df['ts_code'][i],
                        'ann_date': df['ann_date'][i],
                        'max_close': max_close,
                        'min_close': min_close,
                        'the_close': the_close,
                        'eps': df['eps'][i],
                        'total_revenue_ps': df['total_revenue_ps'][i],
                        'undist_profit_ps': df['undist_profit_ps'][i],
                        'gross_margin': df['gross_margin'][i],
                        'fcff': df['fcff'][i],
                        'fcfe': df['fcfe'][i],
                        'tangible_asset': df['tangible_asset'][i],
                        'bps': df['bps'][i],
                        'grossprofit_margin': df['grossprofit_margin'][i],
                        'npta': df['npta'][i],
                        'roic': df['roic'][i],
                    })
            except Exception as e:
                pass
        df2 = pd.DataFrame(new_list)
        print(df2)
        df2.to_csv('daily.csv', index=True)
        
if __name__ == '__main__':
    mu = MysqlUtils()
    ci = classIfication()
    df = ci.get_fina_indicator(mu.conn)
    ci.get_daily(mu.conn, df)